From DevOps to Autonomous Enterprise Platforms: An AI-Orchestrated Architecture for Intelligent Cloud-Native Operations
Journal of Advanced Engineering Technology and Management
ISSSN (Online): 3049-3684
Volume: 2 Issue: 1 | Open Access | 31 August 2026
From DevOps to Autonomous Enterprise Platforms: An AI-Orchestrated Architecture for Intelligent Cloud-Native Operations
Mohit Kumar, Research Scholar, DIT University.
Abstract
Enterprise computing is evolving from infrastructure operations to DevOps, cloud-native engineering, platform engineering, and autonomous platforms. New patterns in large language models (LLMs), intelligent agents, event-driven architectures, Kubernetes, infrastructure as code, and enterprise application integration are accelerating this trend. However, autonomous enterprise platforms are not simply DevOps pipelines enhanced with artificial intelligence (AI). Instead, autonomous platforms require an enterprise architecture where infrastructure automation, application orchestration, enterprise data, AI services, security, governance, and humans-in-the-loop work together as one ecosystem. This article describes an AI orchestrated enterprise architecture for autonomous platforms bridging cloud-native infrastructure, intelligent microservices, enterprise applications, enterprise data services, and AI-driven decision-making. It is grounded in lessons learned from DevOps evolution toward autonomous platforms, production grade Kubernetes engineering, LLM orchestrated microservices, and patterns for enterprise integration from SAP empowered AI architectures. It distinguishes transactional authority with AI intelligence so enterprise systems can continue to be our system of record, while we use AI services for prediction, optimization, reasoning, and decision support. Infrastructure as code, Kubernetes orchestration, event driven execution, LLM based service coordination, security, observability, governance and human-in-the-loop controls are discussed. We establish a trajectory from automation to intelligence orchestration and eventually toward bounded autonomy.
Keywords: DevOps, Autonomous Platforms, Enterprise AI, Cloud-Native Architecture, Kubernetes, Large Language Models, Microservices, Platform Engineering, Infrastructure as Code, Intelligent Automation, Enterprise Architecture
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